The clinical evidence is accumulating since 2015 that anti-diabetic sodium-glucose cotransporter 2 (SGLT2) inhibitors have the beneficial effect of cardiovascular and, recently, renal protection. Although it is not well analyzed how the transfer of this new evidence into daily practice has expedited, we hypothesize that the recent usage of the drugs is positively associated with several certified cardiologists in each region.The 2016 annual and 2016-2017 increased number of SGLT2 inhibitor tablets, based on the National Database of Health Insurance Claims and Specific Health Checkups of Japan, were divided by the estimated number of patients with type 2 diabetes mellitus for each of the 47 prefectures. Then, regression analyses were performed to investigate the potential association of the number of certified cardiologists with the drug prescription.The 2016 prescription of ipragliflozin, dapagliflozin, luseogliflozin, canagliflozin, and empagliflozin was 2.7- to 4.4-fold different between prefectures. The 2016-2017 increased prescription volume also varied among prefectures by as large as 7.3-fold for ipragliflozin. Regression analysis revealed that the annual and increased prescription volume of all the SGLT2 inhibitors except luseogliflozin were higher in regions with more certified cardiologists (P less then 0.05), even after adjusting for regional parameters.In conclusion, the regional number of certified cardiologists was positively associated with a 2016 annual of and 2016-2017 increase in SGLT2 inhibitor prescription amount, implying an early adopter role of clinical experts in healthcare delivery.Severe aortic stenosis (AS) is often accompanied by renal dysfunction, which portends a poor prognosis. Trans-catheter aortic valve replacement (TAVR) is an accepted therapy for patients with severe AS, whereas the prediction of persistent renal dysfunction following TAVR remains challenging. In this study, we aimed to evaluate the pre-procedural score to assess the reversibility of renal dysfunction following TAVR. A total of 2,588 patients with severe AS who received TAVR and were enrolled in the Optimized transCathEter vAlvular iNtervention (OCEAN-TAVI) multicenter registry (UMIN000020423) were retrospectively investigated and those with serum creatinine (Cre) data at baseline and one year following TAVR were included. The Cre score was calculated using the formula 0.2 × (age [years]) + 3.6 × (baseline serum Cre [mg/dL]). This score was evaluated to assess the risk of persistent renal dysfunction defined as serum Cre level &gt; 1.5 mg/dL at one year following TAVR. Of the 1705 patients (84.3 ± 5.0 years old) included, 246 (14%) had persistent renal dysfunction following TAVR. The Cre score predicted the incidence of persistent renal dysfunction with an adjusted incidence rate ratio of 1.48 (95% confidence interval 1.42-1.56) with a cutoff of 21.4 (43% versus 5%, P less then 0.001). The Cre score also predicted 4-year survival following TAVR (70% versus 52%, P less then 0.001) with an adjusted hazard ratio of 1.75 (95% confidence interval 1.29-2.37). In conclusion, the Cre score identified those with a high risk of one-year persistent renal dysfunction following TAVR. The implication of Cre score-guided therapeutic strategy is the next concern.Atrial fibrillation is a clinically important arrhythmia. There are some reports on machine learning models for AF diagnosis using electrocardiogram data. However, few reports have proposed an eXplainable Artificial Intelligence (XAI) model to enable physicians to easily understand the machine learning model's diagnosis results.We developed and validated an XAI-enabled atrial fibrillation diagnosis model based on a convolutional neural network (CNN) algorithm. We used Holter electrocardiogram monitoring data and the gradient-weighted class activation mapping (Grad-CAM) method.Electrocardiogram data recorded from patients between January 4, 2016, and October 31, 2019, totaling 57,273 electrocardiogram waveform slots of 30 seconds each with diagnostic information annotated by cardiologists, were used for training our proposed model. Performance metrics of our AI model for AF diagnosis are as follows sensitivity, 97.1% (95% CI 0.969-0.972); specificity, 94.5% (95% CI 0.943-0.946); accuracy, 95.3% (95% CI 0.952-0.955); positive predictive value, 89.3% (95% CI 0.892-0.897); and F-value, 93.1% (95% CI 0.929-0.933). The area under the receiver operating characteristic curve for AF detection using our model was 0.988 (95% CI 0.987-0.988). https://www.selleckchem.com/products/ubcs039.html Furthermore, using the XAI method, 94.5 ± 3.5% of the areas identified as regions of interest using our machine learning model were identified as characteristic sites for AF diagnosis by cardiologists.AF was accurately diagnosed and favorably explained with Holter ECG waveforms using our proposed CNN-based XAI model. Our study presents another step toward realizing a viable XAI-based detection model for AF diagnoses for use by physicians.The coronavirus disease 2019 (COVID-19) pandemic has changed the lives of healthcare professionals, especially vulnerable physicians such as young or female cardiologists. In Japan, they are facing the fear of not only infection but also weak and unstable employment, difficulties in medical practice and training anxiety, implications for research and studying abroad, as well as worsened mental health issues due to social isolation. Conversely, some positive aspects are seen through the holding of remote meetings and conferences. Here, we suggest a new working style for cardiologists, as well as offer solutions to the medical employment problems that have been taken place in Japan.This study sought to establish whether temperature gradients between the cervix, vagina, and rectum at and 7 days post-artificial insemination (AI) were associated with the incidence of pregnancy in lactating dairy cows (Experiment I; n = 90 ovulating cows) and to evaluate temperature gradient dynamics from the time of insemination to 7 days post-AI under heat stress conditions (Experiment II; n = 16 ovulating and 4 non-ovulating cows). In Experiment I, 39 cows (43.3%) became pregnant. The odds ratio for pregnancy was 2.5 for each one-tenth of a degree drop in cervical temperature with reference to the control rectal temperature at the time of AI (P = 0.01), whereas the same decrease in the cervix-rectum temperature differential 7 days post-AI resulted in ana odds ratio of 0.44 (P = 0.02). In Experiment II, 5 of the ovulating cows (31.3%) became pregnant. The mean values of the vagina-rectum, vagina-cervix, and cervix-rectum temperature differentials at AI (day 0), 8 h, 24 h, and 7 days post-AI changed significantly from day 0 to day 7 (within-subject effect; P less then 0.